122 citations · 540 across the 65 of their papers we have counts for
8 papers · 1 filter
A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
Shridhar Vashishtha, Krishna Prasath Logakannan, Jacob Hochhalter +2
Digital twins are developed to model the behavior of a specific physical asset (or twin), and they can consist of high-fidelity physics-based models or surrogates. A highly accurat…
A Kernel Approach for PDE Discovery and Operator Learning
Da Long, Nicole Mrvaljevic, Shandian Zhe +1
This article presents a three-step framework for learning and solving partial differential equations (PDEs) using kernel methods. Given a training set consisting of pairs of noisy…
Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes
Conor Tillinghast, Zheng Wang, Shandian Zhe
We propose a nonparametric factorization approach for sparsely observed tensors. The sparsity does not mean zero-valued entries are massive or dominated. Rather, it implies the obs…
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation
Zheng Wang, Wei Xing, Robert Kirby +1
The key task of physical simulation is to solve partial differential equations (PDEs) on discretized domains, which is known to be costly. In particular, high-fidelity solutions ar…
Physics Informed Deep Kernel Learning
Zheng Wang, Wei Xing, Robert Kirby +1
Deep kernel learning is a promising combination of deep neural networks and nonparametric function learning. However, as a data driven approach, the performance of deep kernel lear…
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications
Yun Yuan, Xianfeng Terry Yang, Zhao Zhang +1
Despite the wide implementation of machine learning (ML) techniques in traffic flow modeling recently, those data-driven approaches often fall short of accuracy in the cases with a…
Conditional Expectation Propagation
Zheng Wang, Shandian Zhe
Expectation propagation (EP) is a powerful approximate inference algorithm. However, a critical barrier in applying EP is that the moment matching in message updates can be intract…
Asynchronous Distributed Variational Gaussian Processes for Regression
Hao Peng, Shandian Zhe, Yuan Qi
Gaussian processes (GPs) are powerful non-parametric function estimators. However, their applications are largely limited by the expensive computational cost of the inference proce…